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Found 5 papers, 0 papers with code
Weak Collocation Regression for Inferring Stochastic Dynamics with Lévy Noise
In this work, we propose a Weak Collocation Regression (WCR) to explicitly reveal unknown stochastic dynamical systems, i. e., the Stochastic Differential Equation (SDE) with both $\alpha$-stable L\'{e}vy noise and Gaussian noise, from discrete aggregate data.
Neural Born Series Operator for Biomedical Ultrasound Computed Tomography
Ultrasound Computed Tomography (USCT) provides a radiation-free option for high-resolution clinical imaging.
Reconstruction of dynamical systems from data without time labels
In this paper, we study the method to reconstruct dynamical systems from data without time labels.
Weak Collocation Regression method: fast reveal hidden stochastic dynamics from high-dimensional aggregate data
Taking the collocations of Gaussian functions as the test functions in the weak form of the FP equation, we transfer the derivatives to the Gaussian functions and thus approximate the weak form by the expectational sum of the data.
A Deep Learning Approach to Predicting Ventilator Parameters for Mechanically Ventilated Septic Patients
We develop a deep learning approach to predicting a set of ventilator parameters for a mechanically ventilated septic patient using a long and short term memory (LSTM) recurrent neural network (RNN) model.
